Abstract: Aiming at the problem that the current eye-movement tracking methods can not be applied to intelligent mobile phones, tablet computers and other portable devices, an eye-movement tracking method based on large-scale data sets is proposed. Firstly, a large-scale data set is constructed by crowd-sourcing method. Then a deep neural network is trained with the data set for end-to-end prediction. Finally, a smaller and faster network is trained to optimize, which makes the proposed method run in real-time on mobile devices. Experimental results show that the proposed method has better tracking robustness and data generalization ability than other similar methods. The speed of running in mobile devices can reach 10~15 frames per second. The prediction errors of this method are 1.71 cm and 2.53 cm respectively in mobile phone and tablet computer without correction. After calibration, the errors are reduced to 1.34 cm and 2.12 cm respectively.

Key words: eye-movement tracking, crowd-sourcing method, deep neural network, large scale data sets, robustness